It is of general concern whether the automated structure prediction of unknown material without recourse to any knowledge from experiment is ever possible considering the daunting complexity of potential energy surface (PES) of material. Here we demonstrate that the stochastic surface walking (SSW) method can be a general and promising solution to this ultimate goal, which is applied to assemble carbon fullerenes containing up to 100 atoms (including 60, 70, 76, 78, 80, 84, 90, 96, and 100 atoms) from randomly distributed atoms, a long-standing challenge in global optimization. Combining the SSW method with a parallel replica exchange algorithm, we can locate the global minima (GM) of these large fullerenes efficiently without being trapped in numerous energy-nearly degenerate isomers. Detailed analyses on the SSW trajectories allow us to rationalize how and why the SSW method is able to explore the highly complex PES, which highlights the abilities of SSW method for surmounting the high barrier and the preference of SSW trajectories to the low energy pathways. The work demonstrates that the parallel SSW method is a practical tool for predicting unknown materials.