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We previously reported an antibiotic discovery screening platform that identifies whole-cell active compounds with high sensitivity while simultaneously providing mechanistic insight, necessary for hit prioritization. Named PROSPECT, (PRimary screening Of Strains to Prioritize Expanded Chemistry and Targets), this platform measures chemical-genetic interactions between small molecules and pooled Mycobacterium tuberculosis mutants, each depleted of a different essential protein. Here, we introduce Perturbagen CLass (PCL) analysis, a computational method that infers a compound's mechanism-of-action (MOA) by comparing its chemical-genetic interaction profile to those of a curated reference set of 437 known molecules. In leave-one-out cross-validation, we correctly predict MOA with 70% sensitivity and 75% precision, and achieve comparable results (69% sensitivity, 87% precision) with a test set of 75 antitubercular compounds with known MOA previously reported by GlaxoSmithKline (GSK). From 98 additional GSK antitubercular compounds with unknown MOA, we predict 60 to act via a reference MOA and functionally validate 29 compounds predicted to target respiration. Finally, from a set of ~5,000 compounds from larger unbiased libraries, we identify a novel QcrB-targeting scaffold that initially lacked wild-type activity, experimentally confirming this prediction while chemically optimizing this scaffold. PCL analysis of PROSPECT data enables rapid MOA assignment and hit prioritization, streamlining antimicrobial discovery.