Liquid crystals contain rigid mesogenic parts (mesogen) and flexible alkyl parts, which induce the liquid crystalline (LC) state. The aim of this study is to develop models for the prediction of LC behavior applied on a large dataset of rod-like aromatic organic compounds using a QSPR approach. The prediction models are performed using ensemble learning methods with a series of molecular descriptors and chemical fingerprints considering mesogenic parts and flexible alkyl parts of LC structures. This work demonstrates that the complex phenomena of LC phase formation by large variety of mesogens can be effectively modelled using ensemble learning. The best of these models showed high accuracy and F1 score. (90% and 93%) The best model allowing experimentalists to seek the synthesis of predicted molecule that would exhibit the desire LC properties to accelerate the progress in the discovery of new LC materials.