Engineering of GPCR constructs with improved thermostability is a key for successful structural and biochemical studies of this transmembrane protein family, targeted by 40% of all therapeutic drugs. Here we introduce a comprehensive computational approach to effective prediction of stabilizing mutations in GPCRs, named CompoMug, which employs sequence-based analysis, structural information, and a derived machine learning predictor. Tested experimentally on the serotonin 5-HT 2C receptor target, CompoMug predictions resulted in 10 new stabilizing mutations, with an apparent thermostability gain ~8.8˚C for the best single mutation and ~13˚C for a triple mutant. Binding of antagonists confers further stabilization for the triple mutant receptor, with total gains of ~21˚C as compared to wild type apo 5-HT 2C . The predicted mutations enabled crystallization and structure determination for the 5-HT 2C receptor complexes in inactive and active-like states. While CompoMug already shows high 25% hit rate and utility in GPCR structural studies, further improvements are expected with accumulation of structural and mutation data.