Customer Lifetime Value (LTV) prediction is a critical task in online games, as it helps with the formulation of refined game operation strategies, resource allocation, and personalized recommendation. Accurate LTV values enable to identify and target high-value users, enhance user retention and further long-term revenue growth. However, LTV prediction in online games faces unique challenges. Most in-app purchases (IAP) games have various fixed recharge levels, and users with different payment preferences show distinct LTV distributions. Moreover, existing methods fail to capture the multi-modal distribution of LTV values, and suffer from bias when predicting LTV values of games that users have not registered, i.e., new users. To address these challenges, we propose HiLTV, a novel hierarchical framework for LTV prediction in online games. We devise hierarchical modules to align with real-world user recharge behaviors, and a Zero-Inflated Mixture-of-Logistic (ZIMoL) loss instead of a unimodal distribution loss is adopted to better model various segments of users. We also introduce a calibration module that enables more robust predictions for new users. Both offline evaluation on real-world industrial datasets over state-of-the-art baselines and online A/B test from a leading game platform demonstrate the superior performance of our method.