I believe the answer to this depends on your experimental design. By definition, a differential expression analysis is a comparison between two states, usually that you expect to have different biological phenotypes/outcomes. If all three of your libraries represent different experimental conditions that you suspect are independent against the dependent library, then the best way to analyze them is pairwise.
If you have a situation where one effect on one dependent condition is due to multiple other independent conditions, then you can use generalized linear models, which is supported by DESeq. For example, if you suspect your differential result is a result of changes in both your treatment and in the sequencing platform used (let's say you used Illumina to sequence one library and Ion Torrent to sequence the others), then multi-factor analysis would be appropriate:
Differentially Expressed Counts ~ Treatment + Sequencing Type
For my RNA-seq DE analyses, I've done pairwise comparisons for the most part, using DESeq, baySeq, and edgeR, and I looked at: 1. the output of each package, 2. the intersection of any two of the three packages, and 3. the intersection of all three packages. You can get a sense of which of your DE calls are more statistically significant this way. Often, it is worth using more than one method, since they all preprocess and analyze the data in different ways.
So yes, you'll likely end up doing 3 pairwise comparisons for these, unless your experimental design is built for GLMs.
