There are several points to be made regarding your question. I will try my best to address them below.

I know it may sound like nitpicking, but I think it is important to make clear that there is no such thing as "highly homologous". If two proteins share a common evolutionary origin, they are homologous; if they do not, they are not homologous. Homology is binary - two proteins either are or are not homologous. What you can talk about is whether two homologous proteins are highly similar (i.e. have high sequence similarity) or not, but in that case it is entirely your choice how similar two proteins must be to be "highly similar".

Like Stefano said, because homology (and paralogy) is defined by evolution, you normally would have to create evolutionary trees to be able to say anything for sure. However, the reason why one has to do so is to be able to distinguish between orthologs and paralogs (both of which are homologs). However, you are trying to find paralogs within a single organism. Because all your proteins are from the same organism, they by definition cannot be orthologs. In other words, within an organism all homologs are by definition paralogs. I would thus argue that in your specific case there is no need to make phylogenetic trees.

Your question can thus be rephrased to "How high a BLAST score must I have in my database search to infer that two proteins are homologous?". This is a purely statistical question, which E-values were specifically designed to answer. So my advice would be to simply pick an E-value cutoff on the BLAST search in this case. Which one to pick is a tradeoff between sensitivity and specificity - I would normally use something in the range from 1e-3 to 1e-6.

An alternative to this is to not do it yourself. You mention that you work on a model organism, which means that the genome is presumably included in the various orthology databases. You should thus consider extracting paralogs from, for example, the COG/KOG or eggNOG databases.

To consider the score obtained in the alignment to itself as the reference could be a good option. Each protein aligned to itself obtains in BLAST an specific score. To know if the similarity is sufficiently significant to consider the two aligned proteins as paralogs it would be more precise if you refer the obtained score to the score obtained in the comparison with itself. You can calculate for each alignment the ratio between the score of the protein / score of the query protein to itself and then, you can fix a threshold of 0.7, by instance, for selecting the paralogs. With that threshold, in your example, only Protein 2A with Score 300 (300/410=0.73) would be selected as paralog protein for Protein1. To fix an appropriate threshold you can calculate these values for real known paralog proteins and extrapolate a threshold for your analysis.

The other point about the selection of only one alignment for each pair of proteins you can select only the alignment corresponding to the first HSP (HSP_num=1) which is the better because in BLAST(by default) the HSPs of each protein are ordered by E value from better alignment (minor E value) to worst alignment (higher E value) . The HSP with the minor E- value is the HSP with the higher score (perhaps in some very exceptional cases it could not be true).

To find paralogs, you need a phylogenetic program such as Phylip or Paup. With BLAST, collect all sequences with enough similarity, plus an outgroup, a protein that diverged before all the others (the homologue in a non related species like yeast, Arabidopsis or a bacteria if your model organism is mouse) select the conserved motif, use ClustalW and then Phylip/Paup.

Once you have learned how to do it, remember you need bootstrapping to add some statistical significance.

I also answered a related question here that might give you further ideas and keyword to look up.

The assumption is orthologous genes have identical or highly related functions and this sharing is greater than for paralogs. But Nehrt, Hahn et al challenge this by offering that "the most important factor in the evolution of function is not amino acid sequence, but rather the cellular context in which proteins act."

They combined experimentally derived function with gene expression data on nearly 9000 proteins.

This is certainly a controversial statement, but in a thought-provoking manner. After all, it is the integration of diverse data that are driving a lot of genomics. One example is GWAS (genome-wide association studies) + gene expression = better identification of likely causal variant. The same might be applied to the ortholog/paralog definition.