In this study, we investigated the performance of different conformation generation engines, namely Auto3D, CREST, Balloon, and ETKDG (from RDKit), in generating molecular gas phase conformational ensembles by their ability to predict experimental gas-phase collisional cross section values for 20 small molecules. We utilized a computational workflow that encompassed conformer generation, clustering, and analysis of global minima and accurate CCS prediction. The conformations were generated using the respective conformational search engines, and we compared them with the global minima obtained through QM computation that were validated against experimental CCS values. We also compared the predicted CCS values of the generated conformations with experimental values to assess their accuracy. Based on this analysis, we observed that the ML based algorithm, Auto3D achieved the highest success rate in identifying global minima, followed by CREST, ETKDG, and Balloon. In terms of CCS prediction accuracy, QM methods yielded the most accurate results, followed by CREST, Auto3D, ETKDG and Balloon. It is noteworthy that while Auto3D demonstrated a higher success rate in global minima identification, CREST exhibited relatively higher accuracy in CCS prediction among the engines considered.